Image-Only Detection.
For a convincing evaluation, we adopt existing work [1]} that detects visual forgeries in the GAN domain as a comparison.
Note that the GAN-based detector is trained on GAN-generated images and achieve close 0.98 performance on other unknown GAN algorithm  [2]}, [3]}, [4]}, [5]}, [6]}.
fig:cnn depicts the performance of GAN-based detector on fake images generated by diffusion models.
We can observe that most diffusion-generated fake images cannot be detected by the GAN-based detectors.
In all cases, the detection can only achieve an accuracy score of 0.5, which means all fake images are classified as real images.
Therefore, it can be concluded that there is a huge gap between the GAN model and the diffusion model, leading to the complete inability of the GAN-based detector to handle the fake images generated by the diffusion model.
These results also indicate that it is crucial to explore the properties of diffusion models for image attribution.
